An Economist's Journey

Thursday, November 10, 2016

Following the election of Donald Trump, there has been a spat of various explanations as to why he has won. Let me synthesize here the various explanations that I have been able to find so far. I find this discussion fascinating not only for scientific reasons, but also because they speak to what seems to be major changes affecting all the western democracies.

The losers of technological change rebel. Erik Bryjlnofsson has tweeted this graph: the vote for Trump seems to be correlated with how much a share of a county's jobs are routine. David Autor, along with various over coauthors, has shown that a huge change in advanced economies has been the progressive disappearance of middle level routine jobs
yielding to a polarization of the job market: only highly skilled tech
jobs or very low skilled service jobs increase in the economy, while
middle level industry and clerk type jobs are replaced by robots and
computers.

The white majority feels threatened by the rise of the ethnic minorities.
The idea is that the modern welfare state is easier to maintain when
there is ethnic homogeneity. The main reason seems that we are less
generous with people that do not look like us. The increase in the size
of the Hispanic and African-American communities might have triggered a
fear reaction by the white majority that is now trying to protect itself
by restricting access to citizenship or even deporting immigrants.
Recent results for Austria by my IAST colleague and friend Charlotte Cavaillé
seem to give some credit to that explanation. Charlotte and her
coauthors find that natives competing with immigrants for access to
public housing vote more for the far right.

It's Fox News: Simon Wren-Lewis has a very nice post where he discusses recent evidence on the persuasive impact Fox News has on election outcomes.

Democrats simply did not turn out for Hillary. David Yanagizawa-Drott has a put together a really nice graph that shows that Hillary has failed to mobilize Democrat voters.

What to make of all that? Explanations 1 to 3 are the most frightening. They suggest that the model of the modern western welfare state is under jeopardy under the combined forces of globalization (trade and migration) and of technical change. These explanations are extremely dispiriting since it seems extremely hard to conjure up solutions to respond to the challenges raised by these changes. I'd love to think that it is only 6!

Friday, November 20, 2015

Recently, I have embarked in a collective effort to write a blog post on the economics of public policies to fight deforestation as part of a series of posts in preparation of COP21, a joint venture between TSE, Le Monde and The Economist. I have had the chance to work with a team of amazingly cool, witty and nice collaborators (Arthur van Benthem, Eduardo Souza Rodrigues, Julie Subervie). The post has appeared in French here and in English there.

What I would like to do here is to summarize our ideas and then highlight the results of one recent paper by Julie which gives the first robust evidence on the effectiveness of one particularly important policy, Payments for Environmental Services (PES), in the Amazon.

The reasons why we want to stop deforestation are pretty straightforward: deforestation is responsible for about 10 percent of climate change emissions and leads to massive biodiversity losses. Actually, deforestation is not the direct result of a private effort by landowners, it is the result of a massive colonization campaign sponsored by the junta government in Brazil in the 70s.

The key question is which policy to choose to halt deforestation. There are a lot of options. Governments tend to favor regulation, like declaring some land protected and banning all cutting there. Economists call these policies "Command and Control" because they are highly interventionist and leave no leeway to agents to adapt to the policy. Economists favor price instruments above all, such as a carbon market or a carbon tax. The key advantage of these policies is that they let much more leeway to agents to adapt: when there is a price on carbon, the less costly carbon reduction options are the ones implemented first. With command and control, you might impose much higher costs to reach the same environmental gain by banning very profitable cuts of trees and allowing some trees to be cut where economic returns are actually small. PES are an intermediate between command and control policies and price instruments. With PES, governments pay farmers who accept to conserve their trees standing a fixed amount per hectare. Theoretically, PES are less efficient than market instruments, since they leave room for farmers to choose not to receive the incentive, whereas a tax or a price is for everyone to pay. Also, those who volunteer might be the ones who would have not cut their trees anyway even in the absence of payment. If this is widespread, they benefit from a windfall effect: they do nothing and get paid. PES should be better than command and control though (we say that they are second best instruments, actually they are third best since they are linear contracts whereas one could think of nonlinear PES contracts that would be the true second best option).

But this is theory. What we would like to know is whether these predictions hold in real life, right? I mean, that's useful to know how policies work in the perfect, vaccum-full world of models but how do these predictions hold up in reality? Many things can go wrong in the imperfect, air-full realm of the real world. Agents might not be as well-informed or as rational as we assume that they are in our models, tax enforcement might be undermined by corruption or inefficiency.

It turns out that it is extremely difficult to measure the effectiveness of forest conservation policies. Why? Because we face two very serious empirical challenges: additionality and leakage.

Additionality is a key measure of a program success: how much did the policy contribute to halt deforestation where it was implemented? For example, by how much did deforestation decrease in protected areas? Or among farmers subject to a deforestation tax or to a price of carbon? Or among farmers taking up a PES? In order to measure additionality, we have to compute how much deforestation there would have been in the absence of the policy. But this is extremely hard to do since it did NOT happen: the policy was actually implemented. The situation of reference to which we would like to compare what happened has not happened, we call this situation a counterfactual.

Since we cannot directly observe the counterfactual, we are going to try to proxy for it using something observed. We could take as a proxy the situation before the policy was implemented. But this proxy might be very bad. For
example, after the Brazilian government tightened regulatory policies and improved forest monitoring thanks to satellite imagery in the 2000s, deforestation in the Amazon slowed down to approximately half a million hectares annually. It looks like the policy was successful. But, at the same time, lower prices for soybeans and cattle products also reduced incentives to deforest. So what was the main driver of the decrease in deforestation? How much forest did the policy save exactly?

We could also use areas and farmers not involved in the policy as a proxy for the counterfactual situation. But this proxy might be very bad also. For example, even if we observed that farmers who participate in a PES program have lower deforestation rates than those who do not, this does not imply that the scheme actually reduced deforestation. For sure, farmers who stand to profit the least from cutting down their trees are the most likely to sign up for the program. As a result, the program might end up paying some farmers for doing nothing differently from what they would have done anyway. And so the additional impact of the program may very well be small.

Leakage occurs when a conservation policy, which may be successful locally, triggers deforestation elsewhere. For example, a farmer may stop clearing forest on plots that he has contracted under a PES program but at the same time increase deforestation on plots not covered by the contract. On a larger scale, the threat of paying fines to a provincial government may give incentives to farmers or logging firms to move operations to a neighboring province. In such cases, leakage undermines the additionality of conservation programs. Detecting leakage effects is even more difficult than detecting additionality. Indeed, we not only need to compute a counterfactual, but we first and foremost need to detect where the leakages go.

Ok, so additionality and leakage effects are key to be able to rank the policy options in the real world. So what do we know about the additionality and leakage effects for the various forest policies in the Amazon (and in other threatened rainforests)? Not much actually.

As in medicine when testing the efficacy of a new drug, the gold standard of proof in empirical economics is to conduct a Randomized Control Trial (RCT). In a RCT, we randomly select two groups of individuals or regions and implement the policy only for one group, keeping the second as a control. The difference between treatment and control provides a direct measure of the additionality of the policy. RCTs can also be designed to measure leakage. Though RCTs are commonly run to evaluate education or health policies worldwide, there has been only few randomized evaluations of forest policies. Kelsey Jack from Tufts University performed RCTs to assess tree planting subsidies in Malawi and Zambia. To my knowledge, there are no similar results for forest conservation PES, in Brazil or elsewhere. Seema Jayachandran has been conducting an RCT-based evaluation of a forest-conservation PES program in Uganda. The experiment has been
designed to estimate both additionality and leakage effects. We are waiting for her results impatiently.

In the absence of RCTs, economists usually try
to identify naturally occurring events or “experiments” that they hope can
approximate the conditions of an RCT. In a recent paper, soon to be available here, Julie and her coauthors have conducted such a study of one of the first forest-conservation PES ever implemented in the Amazon. The key graph in this paper is the following:

The program was implemented in 2010. What you can see is that the pace of deforestation decreased after 2010 among participants, while it remained the same among non participants. The change in the difference in land cover between participants and comparison communities is a measure of additionality: it is pretty large, an increase of about 10 p.p. Looking at comparison communities, you can see that the path of deforestation has not increased there, while it should have if leakage was present. What seems to have happened is that farmers have started farming more intensively the previously deforested land, and have actually decreased deforestation on new plots. Using these estimates, an estimate of how much carbon was saved and of the value of a ton of carbon, Julie and her coauthors estimate that the benefits of the program exceed its costs.

A couple of comments here. First, this work is a beautiful example of empirical economists at work. This is how we hunt for causality in the absence of RCT. In order to check for the validity of our natural experiments, we try to see if they do not find effects where they should be none. Here, you can see that before 2010, the deforestation trends in participants and comparison communities were parallel. This is supportive of the critical assumption Julie and her coauthors make here: in the absence of the PES, deforestation trends would have remained the same over time. Second, there is still work to do. The measure of forest cover is declarative and does not rely on satellite data. It is still possible that farmers lied about how many hectares they had in forest still standing. The number of observations is small, so precision is small. And if observations happen to be correlated within communities, precision would even be lower. We would also like to know whether these changes in farming practices are going to persist over time or if the deforestation is going to resume as soon as the program stops. Julie is trying to collect new data on the same guys several years later to check this. Third: these are amazingly encouraging results. It seems that we can do something to save the Amazon rainforest after all. Rejoice.

Tuesday, February 10, 2015

Some time ago, I blogged about one of my current projects on land reallocation in France. I have made some progress on this project in the meantime and I am going to report on it here.

I have worked with Elise Maigné, at Inra. Together, and with the help of Eric Cahuzac, we have been able to secure an access to the data on reparcelling events at the commune level. This data has generously been transmitted to us by Nadine Polombo, who has worked together with Marc-André Philippe to digitize the dataset originally in the hands of the French Ministry of Agriculture. Nadine believes that their dataset is the inly one that remains, since the Ministry of Agriculture has decided to destroy the original data and does not take care of reparcelling events any more. Since then, the data have been made accessible through the open data portal of the French government.

First thing is that there has been 22,374 reallocation events in France reported in this dataset. This is huge, since we have 36,681 communes in France. Some communes have actually undergone more than one reallocation event. There are 18,227 communes that have undergone at least one reallocation event. This means that 49.7% of all French communes have undergone at least one reallocation event.

The first issue with the dataset is that I miss some information: the opening date of the reallocation event is missing for 201 events, the closing date for 380 events and both dates are missing for 291 events. So I have 21,502 events with non missing information on the both opening and closing dates of the reallocation event.

Figure 1: Reallocation Events in France

The events with information on the opening date are presented in Figure 1. Reallocation events start with the end of WWII, with this first wave stopping around 1953. A second wave starts in the late 50s and peaks during the 60s. That is the main wave of land reallocation. Then several waves occur in the 70s, 80s and 90s.

Figure 2: First (1) vs Subsequent (2) Reallocation Events

Since some communes have undergone more than one reallocation event, it is interesting to plot the reallocation events depending on whether they are the first or not. This is done in Figure 2. The wave of the 90s seems to be mainly due to reallocation events occurring on communes that have already been reparcelled once. It is possible though that a different portion of the commune has been reparcelled in the two events.

What would be great now is to have an idea of the way reparcelling was rolled out over space and time. It would especially be nice to know which reparcelling events occurred in between 1955, 1970, 1979, 1988, 2000 and 2010, the dates at which agricultural censuses have been conducted in France. I would add 1963 and 1967 as two large surveys have been conducted at these dates. In order to do this, I have to use a GIS software. Since I use Stata to analyse this dataset, I'm going to use its GIS facilities (for the first time). The beautiful map presented on Figure 3 is the result of this exercise.

Figure 3: Map of the Reallocation Events in France

The first striking feature of this map is that land reallocation mainly occurred in the north of France and much less so in the South. One explanation could be that land in the north is much more fertile, but I do not think this exhausts all possible explanations. This will be the topic of subsequent investigation. The second striking feature is how much the timing of land reallocation is spatially autocorrelated. For example, the area around Paris (the Paris basin) seems to have been almost completely reparceled before 1955. The first wave of reparceling thus seems to have been mainly concentrated in this area. The outskirts of the basin are reached progressively during the 60s and 70s.

The cereal growing regions (yellow) seem to have reparcelled very early, while the areas in mixed cultures (light green) have reparcelled more slowly. Finally, forest regions or regions with open range cattle (dark green) have almost not reparcelled.

Obviously, this strong spatial autocorrelation is not good news for studying the causal effect of land reallocation on agricultural technology adoption. Indeed, what would have been great would have been that reparcelling occurs randomly across space, with communes within the Paris basin reparcelling early and others not so that comparing them captures the effect of reparcelling. Here, a raw comparison of reparcelling communes with non reparcelling ones would be biased by the soil qulaity and types of productions. One better comparison would condition on the agricultural zones: comparing communes within the Paris basin with early and late reallocation (if we can find any) is already better. Actually, my idea is to try to use the finest possible grid size to compare close communes with different reparcelling date.

A last striking feature of the data is that sometimes communes undergoing reallocation seem to be aligned like on a line on the map. This is because land reallocation has occurred along a railroad track or a highway, when these infrastructures were built.

The student paper at TSE (TSEconomist) has asked some of us to provide writing tips for students. Here is my take.

I can say that I have not been very good at writing papers until recently, and that practice is the essence of progress. But, there are a few things I can say that I think can help make writing easier.

The first and main thing is: do NOT start writing when you have finished the theoretical/empirical work. This is a rookie mistake that I repeatedly made over the 3 papers I have out now and the 3 others that I am currently writing. This is stupid. Writing should be intricately related to the work itself, and the paper should be written all along the course of the project. (I think we should think in terms of project, not of papers, since a project is made of several papers, and you have to conduct research, not write papers, papers are the outcome, not the goal.)

What I do now is that I blog: first, I blog about a research idea. This makes for a nice post where I have to explain why I think the idea is important, why I should spend time and effort exploring it and why people should be interested by the results. This is maybe the most critical part of any project. This is also the part that most people overlook, especially students. They generally want to rush for the technical things that seem more reassuring instead of taking time to elaborate their intuition about why something is important. Do elaborate on the why of the project. Spend time and effort explaining why this is an important question for economic science, economic policy and why the literature has not found an answer yet and why you think you can solve that with your idea. If you cannot do that, I would say stop and think again. Do you really want to spend one year of effort on something you do not even know why you are doing it? If you do not do this, you will eventually end up repeating previous research with a small tweak, or you are going to lose the reader into the details and lose track of the ambitious and novel idea that you have. With the blog, I usually write updates of the research as I go along, and this keeps me focused on the original idea and on the eventual changes that I might have made. I have found that I, and students also, tend to lose sight of the original goal as we enter the technical aspect of the project, and we bury ourselves in details instead of exploring the deep important research question. So, first advice, write a blog (or write for my blog, or for any blog). Then, writing the paper is just a matter of wrapping things up. It becomes so much easier.

My second advice is: write as if you would explain your research to your grandma. Do use a relaxed tone, avoid technical words. Try talking yourself, your friends, your family, your colleagues, your teachers, anyone, through your research project, as often as you can. Especially confront specialists of your field and see if you can convince them. If you cannot, it does not mean that your idea is stupid, it means that it still is not clear enough.

Friday, February 6, 2015

In a thought-provoking paper, Josh Angrist and Steve Pischke describe the credibility revolution that is currently going on in economics. Having grown in the Haavelmo-Cowles-Heckman tradition of structural econometrics, I have to admit that I resisted the intuitive attraction that this paper had on me. But the more I think about it, the more I can see all that is correct in the view that Josh and Steve defend in their paper, and the more I see myself adapting this view to my own everyday research, and the more I find myself happy about it. The credibility revolution makes a lot of sense to me since I can relate it to the way I was taught biology and physics, and the reasons why I loved these sciences: for their convincing empirical background. I admittedly have my own interpretation of the credibility revolution, that does not fully overlap with that of Josh and Steve. I am going to try to make it clear in what follows.

To me, the credibility revolution means that data and empirical validation are as important as sound and coherent theories. It means that I cannot accept a theoretical proposition unless I have access to repeated tests that it is not rejected in the data. It also means that I do not use tools that have not proven repeatedly that they work.

Let me give three examples in economics. In economics as a behavioral science, a very important tool to model the behavior of agents under uncertainty is the expected utility framework that dates back at least to Bernoulli, who introduced it to solve the Saint Petersburg paradox. von Neumann and Morgenstern have shown that this framework could be rationalized by some simple axioms of behavior. Allais, in a very famous experiment, tested the implication of one of these axioms. What he found was that people consistently rejected this axiom. This results has been reproduced many times since then. This means that the expected utility framework as a scientific description of how people behave has been refuted. This lead to the development of other axioms and other models of behavior under uncertainty, the most famous being Kahneman and Tversky's prospect theory. This does not mean that the expected utility framework is useless for engineering purposes. We seem to have good empirical evidence that it is approximately correct in a lot of situations (readers, feel free to leave references on this type of evidence in the comments). It might be more simple to use it rather than the more complex competing models of behavior that have been proposed since. The only criteria on which we should judge its performance as an engineering tool is by its ability to predict actual choices. We are seeing more and more of this type of crucial tests of our theories, and this is for the best. I think we should emphasize these empirical results in our teaching of economics: they are as important as the underlying theory that they test.

The second example is in economics as engineering: McFadden's random utility model. McFadden used the utility maximization framework to model people's choices of their transportation mode. He modeled the choice of using your car, the bus, your bike or walking as depending on the characteristics of the travels (time to go to work) and your intrinsic preferences for one mode or the other. He estimated the preferences on a dataset of individuals in the San Francisco bay area in 1972. He then used his model to predict what would happen when an additional mode of transportation would be proposed (the subway, or BART). Based on his estimates, he predicted that the market share of the subway would be 6.3%, well below the engineering estimates of the time that rounded around 15%. When the subway opened in 1976, its market share soon reached 6.2% and stabilized there. This is one of the most beautiful and convincing example of testing of an engineering tool in economics. Actually, this amazing performance decided transportation researchers to abandon their old engineering models and use McFadden's. I think it is for this success than Dan was eventually awarded the Nobel prize in economics. We see more and more of this type of tests of structural models, and this is for the best.

The third example is in economics, or rather behavioral, engineering (when I use the term "behavioral," I encompass all the sciences that try to understand man's behavior). From psychology, and increasingly economics, we know that cognitive and non-cognitive (or socio-emotional) skills are malleable all along an individual's lifetime. We believe that it is possible to design interventions that help kids acquire these skills. But one still has to prove that these interventions actually work. That's why psychologists, and more recently economists, use randomized experiments to check whether these interventions actually work. In practice, they randomly select among a group of children the one that are going to receive the intervention (the treatment group) and the ones that are going to stay in the business as usual scenario (the control group). By comparing the outcomes of the treatment anc control group, we can infer the effect of the intervention free of any source of bias since both groups are initially identical thanks to the randomization This is exactly what doctors do to evaluate the effects of drugs. Jim Heckman and Tim Kautz summarize the evidence that we have so far on these experiments. The most famous one is the Perry preschool program, that followed the kids until their forties. The most fascinating finding of this experiment is that by providing a nurturing environment during the early years of the kids' lives (from 3 to 6), the Perry project has been able to change durably the kids' lives. The surprising result is that this change has not been triggered by a change in cognitive skills, but only by a change in non-cognitive skills. This impressive evidence has directed a lot of attention to childhood programs and to the role of non-cognitive skills. Jim Heckman is one of the most ardent proponents of this approach in economics.

The credibility revolution makes sense to me also because of the limitations of Haavelmo's framework. As I already said, trying to infer stable autonomous laws from observational data is impossible, since there is not enough free variation in this data. There are too many unknowns and not enough observations to recover each of them. Haavelmo was well-aware of this problem, but the solution that he and the Cowles Commission advocated-using a priori restrictions to restore identification-was doomed to fail. What we need to learn something about how our theories and our engineering models perform is not a priori restrictions on how the world behaves, but more free and independent information about how the world works. This is basically what Josh's argument is about: think about these restrictions as to make them as convincing as experiments. That's why Josh coined the term natural experiments: the variation in the observed data that we use should be as good as an experiment, not stemming from theory but from luck: the world has offered us some free variation and we can use it to recover something about its deeper relationships.

The problem with the natural experiment approach is that whether we have identified free variation and whether it really can be used to discriminate among theories is highly debatable. Sometimes, we cannot do better, and we have to try to prove that the natural variation is as good as an experiment. But, a lot of the times, we can think of a way of generating free variation ourselves by building a field experiment. And this is exactly what is happening today in economics. All these experiments (or RCTs: Randomized Control Trials) that we see in the field are just ways of generating free variation, with several purposes in mind: testing policies, testing the prediction accuracy of models, testing scientific theories. Some experiments can do several of these things at the same time.

This is an exciting time to do economics. I will post in the future on other early engineering and scientific tests, and I will report on my own and others' research that I find exciting.

Who I am

I am an Assistant Professor a the Toulouse School of Economics. I specialize in Empirical Economics, with applications in Environment and in Labor and Education. You can find more information about me and my work here.

In this blog, I discuss my and others' research. I'm taking you on my journey as an economist, or, as I said in my introductory blog post, I'm opening my scientific kitchen.